This review summarizes recent advances in condensed matter chemistry within the field of macromolecular phase separation and self-assembly， with emphasis on polymer structure and morphology regulation， functional mechanisms of biomolecular condensates， and the application of multiscale theoretical simulations. Studies have shown that the hierarchical structures of polymers are highly sensitive to reaction conditions. Polymerization-induced phase separation and self-assembly represent key strategies for controlling structural evolution， while the coupling of kinetic factors plays a pivotal role in pattern formation. In biological systems， liquid–liquid phase separation of intrinsically disordered proteins and the subsequent formation of condensates are critical for cellular function. These condensates not only regulate biochemical reactions within their compartments but also undergo feedback regulation by these reactions， thereby forming complex dynamic networks. These intricately coupled processes call for more advanced methodologies. In this context， multiscale theoretical simulations， particularly hybrid approaches that integrate molecular dynamics and Monte Carlo methods， provide powerful tools to probe the formation and evolution of hierarchical structures under varying reaction conditions. Despite significant progress， several challenges remain. For instance， precise control over the driving forces of polymer phase separation and high-fidelity simulations of biomolecular condensate structure and function continue to be pressing issues. Future studies should focus on elucidating the dynamic effects of condensed-phase reactions on polymer hierarchical structures， systematically dissecting the structural regulation of biomolecular condensates， and further exploring the impact of chemical modifications on their multiscale organization. Such endeavors will not only deepen our understanding of the fundamental principles of condensed matter chemistry but also provide new theoretical support and research directions for the advancement of this field.
Contents
1 Introduction
2 Condensed phase structures and morphologies of polymers
2.1 Structures and morphologies of polymers
2.2 Mechanisms of polymerization-induced phase separation（ PIPS） and self-assembly（ PISA）
2.3 Role of kinetic coupling in pattern formation
3 Biomolecular condensates and functional phase separation
3.1 Liquid-liquid phase separation （LLPS） of intrinsically disordered proteins
3.2 Formation and function of biomolecular condensates
3.3 Feedback between biochemical reactions and condensate formation
4 Multiscale theoretical modeling approaches
4.1 Coarse-grained modeling of macromolecular systems
4.2 Hybrid molecular dynamics/Monte Carlo （MD/MC）techniques
4.3 Simulation of structure evolution under condensedphase conditions
4.4 Modeling challenges in reactive and biological systems
The term "condensed-phase chemistry" was first proposed by Xu Ruren and others. Its core concept is that the process and products of chemical reactions depend not only on the structure and composition of the reactants themselves, but also on the physical and chemical environment, particularly the joint influence of multilevel structured complex systems［1］. In recent years, condensed-phase chemistry has demonstrated broad prospects in elucidating the structure–function relationships of complex molecular systems and achieving their precise regulation. With the deepening intersection between materials science and life sciences, the condensed-phase behavior of polymers and biomacromolecules has gradually become an important research subject in this field. Unlike traditional phase transition processes, condensed-phase chemical reactions are often accompanied by a multilevel structural dynamic evolution from the molecular scale to the macroscopic scale, which underlies intricate thermodynamic and kinetic coupling mechanisms. Therefore, systematically summarizing the formation mechanisms of macromolecular condensed-phase structures, functional regulation strategies, and corresponding theoretical simulation methods is of great significance for advancing both the theoretical development and experimental design in this field.
In macromolecular systems, phase separation and self-assembly phenomena are ubiquitous and constitute core elements for understanding their condensed-phase behavior. For synthetic macromolecules, polymerization-induced phase separation (PIPS) and polymerization-induced self-assembly (PISA) have emerged as important strategies for constructing ordered morphologies and multilevel structures. In these processes, the coupling among chemical reaction conditions, reaction kinetics, and diffusion behavior directly determines the formation mode and stability of multilevel structures.
In biological systems, liquid-liquid phase separation (LLPS) also exhibits significant functionality. In particular, biomolecular condensates formed by intrinsically disordered proteins not only determine the functional state of cellular organelles through their structural evolution but also enable precise control of life processes by regulating internal biochemical reactions. The essence of these phenomena lies in the synergy between reaction-diffusion processes and molecular recognition, making them one of the important frontiers in current condensed matter chemistry research.
2 The Condensed-Phase Structure and Morphology of Polymers
Polymer condensed-phase structures exhibit complex characteristics across multiple scales and levels. These structural features arise not only from the chemical composition and conformation of chain-like molecules but are also significantly influenced by the evolution of the aggregate morphology. In synthetic chemistry, materials science, and biomedicine, a thorough elucidation of the reaction mechanisms and structure-formation processes of polymers in the condensed phase constitutes a crucial foundation for achieving precise material control and functional design.
In recent years, the development of aggregation-induced phase separation and aggregation-induced self-assembly methods has opened up new avenues for the precise control of polymer structures. In these processes, kinetic factors play a central role in pattern formation and structural evolution. The kinetic processes not only determine the rate and evolutionary pathways of structure formation but also profoundly influence the stability and orderliness of the final structures. Therefore, a systematic exploration of the mechanisms by which kinetic factors govern the formation and regulation of polymer condensed-phase structures will not only help enrich the theoretical framework of polymer science but also provide crucial support for advancing its applications in functional materials and advanced manufacturing.
2.1 Structure and Morphology of Polymers
The structural diversity of polymers is jointly determined by multiple factors, including their fundamental chemical composition, chain configuration, and intermolecular interactions. Based on these factors, polymers can exhibit basic topological structures such as linear, highly branched, or networked forms. By precisely controlling conditions like copolymer composition, polymerization strategies, and solvent environments, researchers can further expand structural complexity, thereby obtaining diverse aggregated morphologies, including spherical, rod-like, layered, and reticulated forms. Figure 1 illustrates the unique molecular structures formed by polymers under different chemical bonding modes. This richness in structure not only reflects the high plasticity inherent in polymer chemistry but also provides a broad platform for functional design and applications in materials science and related fields. Therefore, a deep understanding and effective regulation of the formation mechanisms underlying polymer structural diversity constitute an essential foundation for advancing innovative applications of polymers in high-performance materials, biomedicine, and other cutting-edge areas.
Polymer phase morphology significantly influences material properties, such as mechanical strength, optoelectronic characteristics, and biocompatibility. In describing polymer blend systems, the Flory–Huggins theory provides an important thermodynamic foundation. This theory defines the mixing free energy () and the interaction parameter (χ), successfully characterizing the compatibility between polymer components and its impact on phase behavior (Equation (1)).
in the equation: represent the volume fractions of polymers A and B, respectively; and denote the chain lengths of polymers A and B, respectively, while χis the Flory interaction parameter; kBis the Boltzmann constant. The mixing free energy () consists of entropy and enthalpy contributions, where the entropy contribution reflects the distribution of polymer segments during mixing, and the enthalpy contribution is associated with the interaction energies among the polymer components. The interaction parameter χis a dimensionless quantity that characterizes the strength of interactions between different polymer components. The empirical relationship between χand temperature Tcan be expressed by Equation (2).
in the equation: the term independent of temperature is referred to as the entropy contribution of χ; is termed the enthalpy contribution. When the value of χN for a system exceeds the critical point, macroscopic phase separation occurs in the polymer mixture, leading to the formation of distinct material microstructures. This theory not only elucidates the principles governing the effects of chain length, composition, and temperature on polymer phase behavior but also provides a theoretical foundation for designing self-assembled systems with controllable morphologies.
The Flory–Huggins theory, along with the subsequently developed self-consistent field theory (SCFT), has been widely applied to predict the phase behavior and morphological evolution of polymer systems. For example, in constructing the microphase separation phase diagram of block copolymers, researchers often adjust the block composition ratio, chain length, and temperature, combined with the calculation of the Flory–Huggins interaction parameter χ, to predict the ordered structures that may emerge in the system. As shown in Figure 2, Matsen et al.［2］ systematically mapped out the stable morphological regions of diblock copolymers under different χN conditions based on self-consistent field theory, providing an important theoretical basis for the design and fabrication of polymer self-assembled nanomaterials.
The Flory-Huggins theory and the self-consistent field theory are not only the thermodynamic foundations for understanding polymer mixing behavior, but also, through their integration with simulation methods and experimental studies, have become important theoretical pillars in condensed matter chemistry for achieving "structure–function" regulation in polymer systems. By applying these theories and methods, researchers can more precisely control the self-assembly process of block copolymers, thereby providing new insights for the development of novel functional materials and nanotechnology.
2.2 Aggregation-Induced Phase Separation and Self-Assembly Mechanism
2.2.1 Aggregation-Induced Phase Separation
Aggregation-induced phase separation refers to the phase separation phenomenon that is triggered during a polymerization reaction due to changes in the compatibility among the components within the system. In the PIPS process, the chain segments generated from monomer polymerization cause the product of the Flory-Huggins interaction parameter χ and the polymer chain length N to undergo dynamic variation. When the value of χN increases and exceeds the critical threshold, the tendency for phase separation in the system is significantly enhanced, ultimately leading to a transformation from the initial homogeneous state into a two-phase structure. The morphology formed by this phase separation process exhibits highly reactive control characteristics.
Seo et al.［4］ Polymerized a mixture of multifunctional monomers in the presence of a macromolecular chain transfer agent (macro-CTA), combining PIPS with the in situ formation of block copolymers. By incorporating a chemically etchable block into their design, they successfully fabricated nanoporous materials with a percolating pore structure. Furthermore, the reversible addition-fragmentation chain transfer (RAFT) polymerization technique employed exhibited excellent controllability, enabling linear growth of the block copolymer molecular weight［5］. Figure 3 illustrates a schematic diagram of the entire process.
In recent years, the combination of PIPS technology with advanced manufacturing methods such as 3D printing and electrochemical polymerization has further expanded its application prospects in fields including nanomaterials, biomedical materials, and energy storage materials. To date, PIPS has been successfully applied to the preparation of polymer electrolyte membranes［6］,catalytic materials［7］,mesoporous materials［4］,size-selective semipermeable membranes［8-9］,biomedical materials［10］,fluorescent sensors［11］,and ion-exchange materials［12］,demonstrating superior performance［13-14］.
Meanwhile, the formation mechanism of PIPS has also attracted extensive attention in the field of theoretical simulation. Researchers have employed methods such as Monte Carlo simulations, phase-field models, and reaction–diffusion models to systematically investigate the morphological evolution, pattern formation, and dynamic windows during the PIPS process, thereby revealing the critical influence of the coupling between reaction kinetics and diffusion on the final structural formation. These theoretical studies have provided a solid theoretical foundation for multiscale, programmable structural design. Liu et al.［15］used dissipative particle dynamics (DPD) simulations to systematically examine the effects of polymerization on the phase separation of binary immiscible mixtures, discovering that their phase behavior exhibits high complexity. As the polymerization reaction proceeds, the system experiences a dual effect: on one hand, the thermodynamic driving force is enhanced, promoting phase separation; on the other hand, the viscosity increases, inhibiting phase separation. This results in a dynamic competitive relationship between these two opposing factors. Kawagoe et al.［16］coupled dissipative particle dynamics with a reaction model for curing simulations, successfully reproducing the PIPS process observed experimentally and elucidating the impacts of thermoplastic resin concentration, molecular weight, and curing conditions on the morphology of phase separation. They confirmed the existence of non-uniform strain fields arising from phase separation between resin phases with different moduli. Furthermore, Stevens et al.［17］developed a coarse-grained molecular dynamics model that, by introducing appropriate interaction parameters, systematically examined the flow, phase separation, and structural reorganization of components during the reaction process, thereby uncovering the regulatory principles governing the formation of ordered microstructures.
2.2.2 Aggregation-Induced Self-Assembly
Aggregation-induced self-assembly is an effective strategy for achieving self-assembly during polymerization reactions, thereby forming nanoscale structures. By controlling the ratio of hydrophobic to hydrophilic segments, the degree of polymerization, and the reaction medium conditions, PISA can enable the controllable fabrication of various nanoarchitectures, ranging from spherical and rod-like morphologies to vesicular structures. A significant advantage of PISA lies in its ability to yield stable self-assembled structures under high-concentration conditions, which opens up new possibilities for the large-scale preparation of nanomaterials. During the PISA process, researchers can track the dynamic evolution of the χN value throughout the reaction to analyze the transition pathway of polymer chains from compatibility to phase separation, and further predict the structure and morphology of the resulting nanoparticles.
Qiu et al. ［18］ In a 2023 review, they systematically summarized the research progress of PISA technology in constructing polymer-based biohybrid nanostructures (PBBNs), with particular emphasis on transforming hydrophilic biomolecules (such as proteins and nucleic acids) into controllable self-assembled structures via the "grafting-from" strategy. Figure 4 illustrates the process of preparing PBBNs composed of biomolecule–polymer conjugates (BPCs) using the grafting strategy.
In the theoretical simulation framework of the PISA system, the Flory-Huggins interaction parameter is typically used as the core thermodynamic parameter in the coupling free-energy term to describe the changes in polymer–solvent compatibility and its driving effect on structure formation［19］. For example, when a higher χvalue is assigned to hydrophobic segments relative to the solvent in simulations, the spontaneous formation of micelles or vesicles can be observed, which is in excellent agreement with experimental results［20］. These studies demonstrate that the Flory-Huggins parameter not only serves as the thermodynamic foundation for understanding polymer phase behavior but also acts as a crucial “bridge” linking theoretical simulations with experimental validation, possessing irreplaceable value in the controlled synthesis and functional design of nanomaterials.
2.3 The Role of Dynamical Coupling in Pattern Formation
During the structure formation of polymer materials, a complex coupling relationship often exists between phase separation kinetics and reaction kinetics. This coupling not only determines the microstructural features of the polymer material but also profoundly influences its macroscopic stability and functional performance. In the PIPS or PISA processes, the synergistic interaction among monomer diffusion rate, polymerization reaction rate, and phase separation rate directly determines the type of the final formed structure as well as its degree of orderliness.
Research has shown that when the diffusion rate and the polymerization reaction rate operate on different timescales, the system can evolve into typical patterns such as periodic stripes and spots. This pattern formation phenomenon is usually modeled and predicted using reaction–diffusion equations, where key parameters include the reaction rate constant, the diffusion coefficient, and interfacial tension. By controlling these parameters, researchers can theoretically reproduce the diverse structural evolution processes observed in experiments, thereby providing an important foundation for achieving predictability and designability of polymer morphologies.
The Gray-Scott model is a simple model describing the reaction-diffusion of chemical substances［21］. In this model, chemical substance A is added to the system at a given "feed" rate f, while two molecules of B can "reactively convert" one molecule of A into B (Equation (3)).
in addition, substance B is removed from the system at a given "clearance" rate k. The process of reaction and diffusion between chemicals A and B can be clearly illustrated on a two-dimensional grid. During the simulation, the local concentrations of A and B are calculated at each grid cell, and both chemicals diffuse across the grid; however, the diffusion rate of A is faster than that of B. To simulate this behavior, the concentrations of A and B in each cell are repeatedly updated using the following equations (Equation (4) and Equation (5)).
in the equation: are the concentrations after grid updating; Aand Bare the concentrations before grid updating; and are the diffusion coefficients of substances A and B; and are the concentration gradients of substances A and B. fand krepresent the “supply” rate of A and the “removal” rate of B, respectively, while denotes the simulation time step. As shown in Figure 5,adjusting fand kcan generate a series of distinct steady-state pattern structures.
In addition, both experimental and simulation results indicate that the pattern scale and its orderliness can be effectively regulated by adjusting kinetic parameters such as temperature, illumination, and catalyst concentration. This finding is of significant importance for achieving structurally controllable biomimetic material design and functional gradient material construction in condensed matter chemistry.
3 Biomolecular Condensates and Functional Phase Separation
In the cellular microenvironment, numerous biomolecules can aggregate to form membraneless subcellular structures, known as biomolecular condensates［22-24］. These condensates are believed to form via liquid–liquid phase separation and are widely present in the nucleus, cytoplasm, and organelle interfaces, regulating core biological processes such as transcription［25］, translation［26］, signal transduction［27］, and stress response［28］.
3.1 The liquid–liquid phase separation mechanism of intrinsically disordered proteins
Intrinsically disordered proteins (IDPs), owing to their lack of a stable tertiary structure, exhibit high flexibility and multivalent interaction capabilities, making them key molecular drivers of LLPS［29］. Some IDPs display a strong bias toward specific amino acid compositions, being enriched in particular types of amino acid residues (e.g., glycine, glutamine, and serine). These domains are also referred to as low-complexity domains (LCD)［27, 30–31］. The flexible conformations within LCDs, together with the abundance of identical amino acid residues, satisfy the requirements for driving protein LLPS［29, 32］. As shown in Figure 6, the spontaneous aggregation of IDPs within biomolecular condensates is primarily mediated by noncovalent interactions, including electrostatic forces, π–π stacking, hydrogen bonding, and hydrophobic interactions［33］. Such phase separation behavior typically exhibits high reversibility and environmental responsiveness, enabling rapid responses to external stimuli (e.g., changes in temperature or concentration) and thereby regulating the spatiotemporal distribution of cellular activities. LLPS does not rely on the presence of biological membranes, offering a novel form of cellular spatial organization. Its underlying molecular mechanisms have laid the physical and chemical foundations for functional studies of biomolecular condensates.
3.2 Formation and Function of Biomolecular Condensates
The spatial organization of biomolecular condensates (such as nucleosomes, P-bodies［28］, and the nucleolus) plays a crucial role in cellular regulation. On one hand, these condensates can activate protein activity through local enrichment and conformational regulation, thereby initiating downstream cellular pathways. On the other hand, by compartmentalizing specific biochemical reactions, they can block certain processes, thus achieving fine-tuned control over the intracellular environment. For example, cyclic GMP-AMP synthase (cGAS) rapidly binds to and forms condensates upon detecting abnormal DNA in the cytoplasm, synthesizing cyclic GMP-AMP (cGAMP) and subsequently activating the cGAS–STING signaling pathway, which is one of the core innate immune response pathways［35–36］. Another typical example is stress granules in the cytoplasm. Under stress conditions, cells form stress granule condensates centered around the G3BP protein family, in conjunction with messenger RNA and various protein and nucleic acid components, thereby halting the translation process［37］. This mechanism enables cells to protect themselves in adverse environments by inhibiting the synthesis of potentially harmful proteins.
The physical states of biomolecular condensates include liquid, gel-like, or solid phases, which are closely related to their physiological functions and pathological manifestations within cells. In neurodegenerative diseases such as frontotemporal dementia and Alzheimer’s disease, researchers generally consider the abnormal evolution of condensates to be a key factor contributing to pathological symptoms［34］. For example, in frontotemporal dementia, mutations in TDP-43 often occur in the LCD region, significantly increasing its propensity for aggregation and leading to irreversible aggregation that accumulates over time within neurons. In Alzheimer’s disease, abnormal aggregation of tau protein can form neurofibrillary tangles, which are hallmark pathological features of the disease. Notably, the aggregation and fibrillization of tau protein are closely associated with phase transitions during LLPS, and these transitions may promote the initiation and progression of pathological aberrant aggregation.
3.3 Feedback Regulation Between Biochemical Reactions and Condensate Formation
The formation of condensates is intricately intertwined with biochemical reactions within cells through complex interactions and feedback mechanisms. Condensates not only regulate the progression of biochemical reactions, but changes in reaction products or the local microenvironment can, in turn, influence the stability and composition of condensates［38］. For example, the synthesis of RNA molecules can promote protein aggregation, whereas RNA degradation may induce the disassembly of condensates［39］; post-translational modifications such as phosphorylation［40］, acetylation, and poly(ADP-ribosylation)［41］, among others (Figure 7), can significantly alter the phase-separation capability of intrinsically disordered proteins (IDPs) by modifying their charge distribution, hydrophobicity, or intermolecular interactions［26］.
Taking the transcription process as an example, during the initiation phase, the RNA polymerase initially synthesizes a series of RNAs that typically do not exceed 10 nucleotides in length. This phenomenon is referred to as "abortive transcription initiation." To explain this process, researchers have proposed various models; among them, the model based on biomolecular condensates has provided a novel explanation［25］. In this model, the synthesis of short RNA chains promotes the formation of local condensates; however, as the amount of RNA synthesized gradually increases to a certain threshold, the condensates tend to disassemble, thereby facilitating a smooth transition of the transcription process from the initiation phase to the elongation phase. During this process, the progressive accumulation of newly synthesized RNA leads to an increase in local negative charge concentration and plays a crucial role in regulating transcriptional dynamics.
This RNA-concentration-driven feedback regulatory mechanism constructs a "structure-function-regulation" coupled network, enabling cells to dynamically regulate condensate states according to functional demands, thereby achieving fine-tuned control over reaction pathways. This complex and highly coordinated mechanism not only exemplifies the paradigm of condensed-phase chemistry within living systems but also offers a new perspective for understanding cellular homeostasis maintenance and disease pathogenesis, while simultaneously posing higher requirements for relevant theoretical models and simulation methodologies.
4 Multiscale Theoretical Simulation Methods
In the field of condensed matter chemistry, particularly concerning the phase separation, self-assembly, and condensate formation dynamics of complex polymers and biomacromolecules, traditional experimental techniques often struggle to precisely capture structural evolution at the nanoscale to micrometer scale, as well as its underlying driving forces［42］. To overcome this limitation, multiscale theoretical simulation methods have demonstrated unique value. These approaches enable coupled modeling across different physical scales, thereby providing robust computational tools for elucidating and predicting the structure–function relationships in such complex systems.
4.1 Coarse-grained simulation of macromolecular systems
Coarse-grained (CG) modeling significantly reduces computational complexity by mapping groups of atoms or structural building blocks to single coarse-grained particles［43-44］. This strategy has been widely applied in the study of polymer phase separation, self-assembly systems, and biomolecular condensates. For example, coarse-grained molecular dynamics simulations based on the Martini force field［45-46］, dissipative particle dynamics methods, and structure-consistent adaptive coarse-graining strategies［47］have all been used to explore macromolecular aggregation behaviors over long time scales and large system sizes. Coarse-grained models can effectively reproduce the phase behavior of polymer solutions and the dynamic reorganization processes of biomolecular condensates, serving as an important bridge between microscopic interactions and macroscopic phenomena.
4.2 Hybrid Molecular Dynamics/Monte Carlo (MD/MC) Techniques
Hybrid simulation strategies that combine molecular dynamics (MD) with Monte Carlo (MC) methods provide a pathway for modeling complex systems that balances both fine detail and computational efficiency. MD is well-suited to characterizing the continuous dynamical trajectories of a system on a given potential energy surface, whereas MC can effectively sample the polymorphic conformations and aggregate-state space of the system［48］. In simulations of polymer-induced phase separation, biomolecular condensates, and reactive systems, the MD/MC hybrid approach can more accurately reproduce the multilevel structural evolution of the system, particularly when exploring pattern formation, nucleation and growth under coupled dynamical and thermodynamic influences, as well as the mechanisms by which chemical reactions affect the establishment of steady-state structures［49］.
4.3 Simulation of Structural Evolution under Condensed-Phase Conditions
Condensed matter systems are typically in non-uniform states with coupled multiple interactions and under non-equilibrium conditions. The simulation of their structural evolution must simultaneously consider multiple physical scales. Common methods include reaction dynamics simulations, studies on morphological evolution under tensile/shear forces, and predictions of structural responses to external stimuli such as different solvents, pH levels, and electric fields. For example, during the PISA process, simulating monomer diffusion, block chain growth, and changes in interfacial tension can reveal the kinetic mechanisms underlying nanoparticle formation. In contrast, when simulating the LLPS behavior of IDPs, it is necessary to account for the synergistic regulatory effects of weak intermolecular interactions and thermal fluctuations on aggregate structures. Such research not only provides a theoretical basis for optimizing material synthesis conditions but also serves as an important reference for the simulation and analysis of biological processes.
4.4 Reactivity and Challenges in Biological Systems Modeling
Although theoretical simulations have achieved breakthrough progress in multiple fields of natural science, they still face several challenges when modeling chemical reactions and complex biological systems［42］. First, the accurate construction of reactive force fields heavily relies on experimental data and high-precision quantum chemical calculations, which limits their generality and applicability. Second, the heterogeneity, non-equilibrium nature, and multi-component cooperative interactions of biomolecular condensates make the construction and parameterization of simulation models even more complex. Furthermore, the trade-off in simulation scale remains a bottleneck: atomistic models, while highly accurate, require enormous computational resources, whereas coarse-grained models, though more efficient, struggle to capture critical chemical details. Therefore, there is an urgent need for future developments in adaptive-accuracy approaches, multi-resolution coupling methods, and machine-learning-driven modeling techniques to better address the theoretical simulation requirements of complex condensed-phase chemical systems.
5 Conclusion and Future Work
This paper focuses on the condensed-phase chemistry involved in phase separation and self-assembly processes within macromolecular systems. It systematically summarizes the current state of research on polymer structure and morphology control, the formation and functions of biomacromolecular condensates, and the development of multiscale simulation methods. By integrating theoretical and experimental approaches with the coupling of thermodynamics and kinetics, the study elucidates the underlying principles governing how chemical reactions influence the multilevel structural organization of macromolecular aggregates. These findings hold significant implications for materials design and the mechanistic understanding of biological systems.
In polymer systems, PIPS and PISA provide effective approaches for the precise control of material morphology and properties. During the structural evolution process, the synergistic interplay between kinetic and thermodynamic factors plays a decisive role. In biological systems, liquid–liquid phase separation of biomacromolecules reveals the physical basis of membraneless organelles, with their condensed states closely associated with cellular functions. Biomacromolecular condensates not only serve as spatial organization platforms for biochemical reactions but can also undergo irreversible aggregation transitions under pathological conditions, thereby participating in disease pathogenesis. The development of multiscale theoretical simulations has provided strong support for understanding the structural formation and regulatory mechanisms of these complex systems.
Despite the significant progress made in recent years in condensed matter chemistry regarding macromolecular phase separation and self-assembly, numerous challenges remain. First, the driving forces behind macromolecular phase separation and their regulatory principles are still unclear. In particular, the microscopic mechanisms in complex copolymer systems, charged systems, and reversible polymer systems require further elucidation. Second, the structure–function relationship of biomacromolecular condensates remains ambiguous. Establishing the connections among IDP sequence features, post-translational modifications, and condensation behavior is currently a major focus as well as a key challenge in this field. Furthermore, the highly complex interactions within multicomponent systems necessitate theoretical simulations that rely heavily on numerous approximations and assumptions, making it difficult to achieve rigorous quantitative comparisons with experimental results. Moreover, modeling condensed matter chemical reactions requires spanning multiple temporal and spatial scales, and existing models are still insufficient to provide a comprehensive description.
Looking ahead, the advancement of this field hinges on breakthroughs in three key directions. First, it is essential to develop a theory of dynamic structural regulation that shifts the focus from static structure prediction to theoretical descriptions of dynamic evolutionary processes, with particular emphasis on the behavior of polymers and biomacromolecules under non-equilibrium conditions. Second, there is a need to advance system-coupled models integrating structure, function, and regulation, by combining high-throughput experiments, machine learning algorithms, and multi-scale simulations to achieve cross-scale modeling of complex systems. Third, close integration between theory and experiment should be promoted, so as to uncover universal principles while simultaneously supporting functional materials design and the elucidation of biomedical problems.
In summary, the study of condensed-state chemistry in macromolecular phase separation and self-assembly not only provides a solid theoretical foundation for understanding complex material behaviors but also opens up new research opportunities in interdisciplinary fields such as materials science, biophysics, and medicine. With the continuous emergence of new theories and tools, this field is expected to advance toward the goal of “controllable structure, designable function, and predictable behavior,” becoming one of the important growth areas in chemistry and related disciplines.
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